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dc.contributor.authorYu, Bo
dc.contributor.authorMak, Terrence
dc.contributor.authorLi, Xiangyu
dc.contributor.authorSmith, Leslie
dc.contributor.authorSun, Yihe
dc.contributor.authorPoon, Chi-Sang
dc.date.accessioned2012-07-23T20:36:28Z
dc.date.available2012-07-23T20:36:28Z
dc.date.issued2012-04
dc.date.submitted2011-12
dc.identifier.issn1475-925X
dc.identifier.urihttp://hdl.handle.net/1721.1/71767
dc.description.abstractBackground Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants.en_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC grant EP/E044662/1)en_US
dc.description.sponsorshipNational Natural Science Foundation (China) ( grant 61006021)en_US
dc.description.sponsorshipNational Natural Science Foundation (China) (grant 4112029)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant HL067966)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant RR028241)en_US
dc.publisherBioMed Central Ltden_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1475-925X-11-18en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.sourceBioMed Central Ltden_US
dc.titleStream-based Hebbian Eigenfilter for real-time neuronal spike discriminationen_US
dc.typeArticleen_US
dc.identifier.citationYu, Bo et al. “Stream-based Hebbian Eigenfilter for Real-time Neuronal Spike Discrimination.” BioMedical Engineering OnLine 11.1 (2012): 18. Web.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.mitauthorPoon, Chi-Sang
dc.relation.journalBioMedical Engineering OnLineen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2012-05-15T15:03:46Z
dc.language.rfc3066en
dc.rights.holderYu et al.; licensee BioMed Central Ltd.
dspace.orderedauthorsYu, Bo; Mak, Terrence; Li, Xiangyu; Smith, Leslie; Sun, Yihe; Poon, Chi-Sangen
mit.licensePUBLISHER_CCen_US


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